Due to potentially very large entity space (e.g. Wikipedia has more than 4 million entities), standard entity linking is often divided into two stages: candidate generation which chooses potential candidates \(C_i=(e_{i1},...,e_{il_i})\)  using a heuristic and entity disambiguation which learns to select the best entity from the candidates using a statistical model. In this work, we focus on the second stage entity disambiguation. As for entity disambiguation, two different kinds of information can be leveraged: local context compatibility and document-level global coherence which respectively corresponds to the local model and the global model. Next, we introduce the general formulation of entity linking problem with a focus on the well known DeepED model [1]}.
